Overview

Dataset statistics

Number of variables39
Number of observations260601
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.5 MiB
Average record size in memory312.0 B

Variable types

Numeric9
Categorical30

Alerts

count_floors_pre_eq is highly correlated with height_percentageHigh correlation
height_percentage is highly correlated with count_floors_pre_eqHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
count_floors_pre_eq is highly correlated with height_percentageHigh correlation
height_percentage is highly correlated with count_floors_pre_eqHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
count_floors_pre_eq is highly correlated with height_percentageHigh correlation
height_percentage is highly correlated with count_floors_pre_eqHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
ground_floor_type is highly correlated with has_superstructure_cement_mortar_brickHigh correlation
other_floor_type is highly correlated with roof_typeHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
roof_type is highly correlated with other_floor_type and 1 other fieldsHigh correlation
has_superstructure_rc_engineered is highly correlated with foundation_typeHigh correlation
has_superstructure_mud_mortar_stone is highly correlated with foundation_typeHigh correlation
foundation_type is highly correlated with roof_type and 4 other fieldsHigh correlation
has_superstructure_cement_mortar_brick is highly correlated with ground_floor_type and 1 other fieldsHigh correlation
has_superstructure_rc_non_engineered is highly correlated with foundation_typeHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
geo_level_1_id is highly correlated with foundation_typeHigh correlation
count_floors_pre_eq is highly correlated with height_percentage and 1 other fieldsHigh correlation
height_percentage is highly correlated with count_floors_pre_eq and 1 other fieldsHigh correlation
foundation_type is highly correlated with geo_level_1_id and 2 other fieldsHigh correlation
roof_type is highly correlated with foundation_type and 2 other fieldsHigh correlation
ground_floor_type is highly correlated with foundation_type and 1 other fieldsHigh correlation
other_floor_type is highly correlated with count_floors_pre_eq and 6 other fieldsHigh correlation
position is highly correlated with has_superstructure_mud_mortar_brickHigh correlation
has_superstructure_mud_mortar_stone is highly correlated with other_floor_type and 2 other fieldsHigh correlation
has_superstructure_mud_mortar_brick is highly correlated with position and 1 other fieldsHigh correlation
has_superstructure_cement_mortar_brick is highly correlated with other_floor_type and 1 other fieldsHigh correlation
has_superstructure_timber is highly correlated with has_superstructure_bambooHigh correlation
has_superstructure_bamboo is highly correlated with has_superstructure_timberHigh correlation
has_superstructure_rc_non_engineered is highly correlated with other_floor_typeHigh correlation
has_superstructure_rc_engineered is highly correlated with other_floor_typeHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
building_id has unique values Unique
geo_level_1_id has 4011 (1.5%) zeros Zeros
age has 26041 (10.0%) zeros Zeros
count_families has 20862 (8.0%) zeros Zeros

Reproduction

Analysis started2022-04-28 13:34:26.929837
Analysis finished2022-04-28 13:36:23.133926
Duration1 minute and 56.2 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

building_id
Real number (ℝ≥0)

UNIQUE

Distinct260601
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525675.4828
Minimum4
Maximum1052934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:23.327168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile52114
Q1261190
median525757
Q3789762
95-th percentile1000724
Maximum1052934
Range1052930
Interquartile range (IQR)528572

Descriptive statistics

Standard deviation304544.999
Coefficient of variation (CV)0.5793403136
Kurtosis-1.203878964
Mean525675.4828
Median Absolute Deviation (MAD)264277
Skewness0.001882356737
Sum1.369915565 × 1011
Variance9.274765644 × 1010
MonotonicityNot monotonic
2022-04-28T15:36:23.784518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8029061
 
< 0.1%
6802961
 
< 0.1%
8025311
 
< 0.1%
5449021
 
< 0.1%
8232571
 
< 0.1%
3735401
 
< 0.1%
6275901
 
< 0.1%
4219511
 
< 0.1%
2411911
 
< 0.1%
10246991
 
< 0.1%
Other values (260591)260591
> 99.9%
ValueCountFrequency (%)
41
< 0.1%
81
< 0.1%
121
< 0.1%
161
< 0.1%
171
< 0.1%
251
< 0.1%
281
< 0.1%
311
< 0.1%
341
< 0.1%
361
< 0.1%
ValueCountFrequency (%)
10529341
< 0.1%
10529311
< 0.1%
10529291
< 0.1%
10529261
< 0.1%
10529211
< 0.1%
10529151
< 0.1%
10529111
< 0.1%
10529091
< 0.1%
10529081
< 0.1%
10529061
< 0.1%

geo_level_1_id
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.90035341
Minimum0
Maximum30
Zeros4011
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:24.103952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median12
Q321
95-th percentile27
Maximum30
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.033616625
Coefficient of variation (CV)0.5779433361
Kurtosis-1.213248785
Mean13.90035341
Median Absolute Deviation (MAD)6
Skewness0.2725303548
Sum3622446
Variance64.53899608
MonotonicityNot monotonic
2022-04-28T15:36:24.382886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
624381
 
9.4%
2622615
 
8.7%
1022079
 
8.5%
1721813
 
8.4%
819080
 
7.3%
718994
 
7.3%
2017216
 
6.6%
2114889
 
5.7%
414568
 
5.6%
2712532
 
4.8%
Other values (21)72434
27.8%
ValueCountFrequency (%)
04011
 
1.5%
12701
 
1.0%
2931
 
0.4%
37540
 
2.9%
414568
5.6%
52690
 
1.0%
624381
9.4%
718994
7.3%
819080
7.3%
93958
 
1.5%
ValueCountFrequency (%)
302686
 
1.0%
29396
 
0.2%
28265
 
0.1%
2712532
4.8%
2622615
8.7%
255624
 
2.2%
241310
 
0.5%
231121
 
0.4%
226252
 
2.4%
2114889
5.7%

geo_level_2_id
Real number (ℝ≥0)

Distinct1414
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.0746851
Minimum0
Maximum1427
Zeros38
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:24.702535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69
Q1350
median702
Q31050
95-th percentile1377
Maximum1427
Range1427
Interquartile range (IQR)700

Descriptive statistics

Standard deviation412.7107336
Coefficient of variation (CV)0.5886829782
Kurtosis-1.188232475
Mean701.0746851
Median Absolute Deviation (MAD)349
Skewness0.02895738139
Sum182700764
Variance170330.1496
MonotonicityNot monotonic
2022-04-28T15:36:25.044940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
394038
 
1.5%
1582520
 
1.0%
1812080
 
0.8%
13872040
 
0.8%
1571897
 
0.7%
3631760
 
0.7%
4631740
 
0.7%
6731704
 
0.7%
5331684
 
0.6%
8831626
 
0.6%
Other values (1404)239512
91.9%
ValueCountFrequency (%)
038
 
< 0.1%
1204
0.1%
377
 
< 0.1%
4315
0.1%
525
 
< 0.1%
62
 
< 0.1%
7100
 
< 0.1%
8120
 
< 0.1%
9333
0.1%
10354
0.1%
ValueCountFrequency (%)
14276
 
< 0.1%
1426286
0.1%
1425466
0.2%
14247
 
< 0.1%
14233
 
< 0.1%
1422216
0.1%
1421254
0.1%
142010
 
< 0.1%
141995
 
< 0.1%
1418152
 
0.1%

geo_level_3_id
Real number (ℝ≥0)

Distinct11595
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6257.876148
Minimum0
Maximum12567
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:25.409916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile611
Q13073
median6270
Q39412
95-th percentile11927
Maximum12567
Range12567
Interquartile range (IQR)6339

Descriptive statistics

Standard deviation3646.369645
Coefficient of variation (CV)0.5826848532
Kurtosis-1.213896506
Mean6257.876148
Median Absolute Deviation (MAD)3171
Skewness0.0003935120899
Sum1630808782
Variance13296011.59
MonotonicityNot monotonic
2022-04-28T15:36:25.790557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
633651
 
0.2%
9133647
 
0.2%
621530
 
0.2%
11246470
 
0.2%
2005466
 
0.2%
11440455
 
0.2%
7723443
 
0.2%
9229381
 
0.1%
2452349
 
0.1%
12258312
 
0.1%
Other values (11585)255897
98.2%
ValueCountFrequency (%)
02
 
< 0.1%
16
 
< 0.1%
39
 
< 0.1%
514
 
< 0.1%
621
 
< 0.1%
72
 
< 0.1%
831
< 0.1%
93
 
< 0.1%
101
 
< 0.1%
1162
< 0.1%
ValueCountFrequency (%)
125671
 
< 0.1%
125657
 
< 0.1%
125646
 
< 0.1%
1256324
< 0.1%
125623
 
< 0.1%
1256119
< 0.1%
1256017
 
< 0.1%
125596
 
< 0.1%
125586
 
< 0.1%
1255744
< 0.1%

count_floors_pre_eq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.129723217
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:26.079883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7276645453
Coefficient of variation (CV)0.3416709456
Kurtosis2.322597881
Mean2.129723217
Median Absolute Deviation (MAD)0
Skewness0.8341129586
Sum555008
Variance0.5294956905
MonotonicityNot monotonic
2022-04-28T15:36:26.337494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2156623
60.1%
355617
 
21.3%
140441
 
15.5%
45424
 
2.1%
52246
 
0.9%
6209
 
0.1%
739
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
140441
 
15.5%
2156623
60.1%
355617
 
21.3%
45424
 
2.1%
52246
 
0.9%
6209
 
0.1%
739
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
81
 
< 0.1%
739
 
< 0.1%
6209
 
0.1%
52246
 
0.9%
45424
 
2.1%
355617
 
21.3%
2156623
60.1%
140441
 
15.5%

age
Real number (ℝ≥0)

ZEROS

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.53502865
Minimum0
Maximum995
Zeros26041
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:26.669014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median15
Q330
95-th percentile60
Maximum995
Range995
Interquartile range (IQR)20

Descriptive statistics

Standard deviation73.56593652
Coefficient of variation (CV)2.772408408
Kurtosis157.2482363
Mean26.53502865
Median Absolute Deviation (MAD)10
Skewness12.19249422
Sum6915055
Variance5411.947016
MonotonicityNot monotonic
2022-04-28T15:36:27.003228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1038896
14.9%
1536010
13.8%
533697
12.9%
2032182
12.3%
026041
10.0%
2524366
9.3%
3018028
6.9%
3510710
 
4.1%
4010559
 
4.1%
507257
 
2.8%
Other values (32)22855
8.8%
ValueCountFrequency (%)
026041
10.0%
533697
12.9%
1038896
14.9%
1536010
13.8%
2032182
12.3%
2524366
9.3%
3018028
6.9%
3510710
 
4.1%
4010559
 
4.1%
454711
 
1.8%
ValueCountFrequency (%)
9951390
0.5%
200106
 
< 0.1%
1952
 
< 0.1%
1903
 
< 0.1%
1851
 
< 0.1%
1807
 
< 0.1%
1755
 
< 0.1%
1706
 
< 0.1%
1652
 
< 0.1%
1606
 
< 0.1%

area_percentage
Real number (ℝ≥0)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.018050583
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:27.357144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q39
95-th percentile16
Maximum100
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.392230936
Coefficient of variation (CV)0.5477928694
Kurtosis30.43825794
Mean8.018050583
Median Absolute Deviation (MAD)2
Skewness3.526082314
Sum2089512
Variance19.29169259
MonotonicityNot monotonic
2022-04-28T15:36:27.716511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642013
16.1%
736752
14.1%
532724
12.6%
828445
10.9%
922199
8.5%
419236
7.4%
1015613
 
6.0%
1113907
 
5.3%
311837
 
4.5%
127581
 
2.9%
Other values (74)30294
11.6%
ValueCountFrequency (%)
190
 
< 0.1%
23181
 
1.2%
311837
 
4.5%
419236
7.4%
532724
12.6%
642013
16.1%
736752
14.1%
828445
10.9%
922199
8.5%
1015613
 
6.0%
ValueCountFrequency (%)
1001
 
< 0.1%
963
< 0.1%
901
 
< 0.1%
865
< 0.1%
854
< 0.1%
843
< 0.1%
833
< 0.1%
821
 
< 0.1%
801
 
< 0.1%
781
 
< 0.1%

height_percentage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.434365179
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:28.022367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile9
Maximum32
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.918418221
Coefficient of variation (CV)0.3530160667
Kurtosis14.31852616
Mean5.434365179
Median Absolute Deviation (MAD)1
Skewness1.808261757
Sum1416201
Variance3.68032847
MonotonicityNot monotonic
2022-04-28T15:36:28.296096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
578513
30.1%
646477
17.8%
437763
14.5%
735465
13.6%
325957
 
10.0%
813902
 
5.3%
29305
 
3.6%
95376
 
2.1%
104492
 
1.7%
11917
 
0.4%
Other values (17)2434
 
0.9%
ValueCountFrequency (%)
29305
 
3.6%
325957
 
10.0%
437763
14.5%
578513
30.1%
646477
17.8%
735465
13.6%
813902
 
5.3%
95376
 
2.1%
104492
 
1.7%
11917
 
0.4%
ValueCountFrequency (%)
3275
< 0.1%
311
 
< 0.1%
282
 
< 0.1%
262
 
< 0.1%
253
 
< 0.1%
244
 
< 0.1%
2311
 
< 0.1%
2113
 
< 0.1%
2033
< 0.1%
197
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
t
216757 
n
35528 
o
 
8316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowo
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
t216757
83.2%
n35528
 
13.6%
o8316
 
3.2%

Length

2022-04-28T15:36:28.673130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:28.834456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
t216757
83.2%
n35528
 
13.6%
o8316
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

foundation_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
r
219196 
w
 
15118
u
 
14260
i
 
10579
h
 
1448

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowr
2nd rowr
3rd rowr
4th rowr
5th rowr

Common Values

ValueCountFrequency (%)
r219196
84.1%
w15118
 
5.8%
u14260
 
5.5%
i10579
 
4.1%
h1448
 
0.6%

Length

2022-04-28T15:36:28.996257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:29.166101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
r219196
84.1%
w15118
 
5.8%
u14260
 
5.5%
i10579
 
4.1%
h1448
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

roof_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
n
182842 
q
61576 
x
 
16183

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rown
2nd rown
3rd rown
4th rown
5th rown

Common Values

ValueCountFrequency (%)
n182842
70.2%
q61576
 
23.6%
x16183
 
6.2%

Length

2022-04-28T15:36:29.345608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:29.509141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n182842
70.2%
q61576
 
23.6%
x16183
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ground_floor_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
f
209619 
x
24877 
v
24593 
z
 
1004
m
 
508

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowx
3rd rowf
4th rowf
5th rowf

Common Values

ValueCountFrequency (%)
f209619
80.4%
x24877
 
9.5%
v24593
 
9.4%
z1004
 
0.4%
m508
 
0.2%

Length

2022-04-28T15:36:29.673523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:29.842180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
f209619
80.4%
x24877
 
9.5%
v24593
 
9.4%
z1004
 
0.4%
m508
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

other_floor_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
q
165282 
x
43448 
j
39843 
s
 
12028

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowq
2nd rowq
3rd rowx
4th rowx
5th rowx

Common Values

ValueCountFrequency (%)
q165282
63.4%
x43448
 
16.7%
j39843
 
15.3%
s12028
 
4.6%

Length

2022-04-28T15:36:30.024062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:30.187713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
q165282
63.4%
x43448
 
16.7%
j39843
 
15.3%
s12028
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

position
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
s
202090 
t
42896 
j
 
13282
o
 
2333

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rows
3rd rowt
4th rows
5th rows

Common Values

ValueCountFrequency (%)
s202090
77.5%
t42896
 
16.5%
j13282
 
5.1%
o2333
 
0.9%

Length

2022-04-28T15:36:30.359490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:30.523071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
s202090
77.5%
t42896
 
16.5%
j13282
 
5.1%
o2333
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
d
250072 
q
 
5692
u
 
3649
s
 
346
c
 
325
Other values (5)
 
517

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd
2nd rowd
3rd rowd
4th rowd
5th rowd

Common Values

ValueCountFrequency (%)
d250072
96.0%
q5692
 
2.2%
u3649
 
1.4%
s346
 
0.1%
c325
 
0.1%
a252
 
0.1%
o159
 
0.1%
m46
 
< 0.1%
n38
 
< 0.1%
f22
 
< 0.1%

Length

2022-04-28T15:36:30.691616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:30.876343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
d250072
96.0%
q5692
 
2.2%
u3649
 
1.4%
s346
 
0.1%
c325
 
0.1%
a252
 
0.1%
o159
 
0.1%
m46
 
< 0.1%
n38
 
< 0.1%
f22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
237500 
1
 
23101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0237500
91.1%
123101
 
8.9%

Length

2022-04-28T15:36:31.106436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:31.261816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0237500
91.1%
123101
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_mud_mortar_stone
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
198561 
0
62040 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1198561
76.2%
062040
 
23.8%

Length

2022-04-28T15:36:31.410787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:31.568922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1198561
76.2%
062040
 
23.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
251654 
1
 
8947

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0251654
96.6%
18947
 
3.4%

Length

2022-04-28T15:36:31.720578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:31.879353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0251654
96.6%
18947
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
255849 
1
 
4752

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0255849
98.2%
14752
 
1.8%

Length

2022-04-28T15:36:32.031815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:32.189539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0255849
98.2%
14752
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_mud_mortar_brick
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
242840 
1
 
17761

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0242840
93.2%
117761
 
6.8%

Length

2022-04-28T15:36:32.342757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:32.499919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0242840
93.2%
117761
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_cement_mortar_brick
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
240986 
1
 
19615

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0240986
92.5%
119615
 
7.5%

Length

2022-04-28T15:36:32.753469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:32.909950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0240986
92.5%
119615
 
7.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_timber
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
194151 
1
66450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0194151
74.5%
166450
 
25.5%

Length

2022-04-28T15:36:33.060656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:33.217196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0194151
74.5%
166450
 
25.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_bamboo
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
238447 
1
 
22154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0238447
91.5%
122154
 
8.5%

Length

2022-04-28T15:36:33.368927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:33.529213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0238447
91.5%
122154
 
8.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_rc_non_engineered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
249502 
1
 
11099

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0249502
95.7%
111099
 
4.3%

Length

2022-04-28T15:36:33.676772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:33.833521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0249502
95.7%
111099
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_rc_engineered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
256468 
1
 
4133

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0256468
98.4%
14133
 
1.6%

Length

2022-04-28T15:36:33.983814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:34.144060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0256468
98.4%
14133
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
256696 
1
 
3905

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0256696
98.5%
13905
 
1.5%

Length

2022-04-28T15:36:34.295625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:34.453264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0256696
98.5%
13905
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
v
250939 
a
 
5512
w
 
2677
r
 
1473

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowv
2nd rowv
3rd rowv
4th rowv
5th rowv

Common Values

ValueCountFrequency (%)
v250939
96.3%
a5512
 
2.1%
w2677
 
1.0%
r1473
 
0.6%

Length

2022-04-28T15:36:34.604378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:34.774533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
v250939
96.3%
a5512
 
2.1%
w2677
 
1.0%
r1473
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

count_families
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9839486418
Minimum0
Maximum9
Zeros20862
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2022-04-28T15:36:34.937384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4183889779
Coefficient of variation (CV)0.425214244
Kurtosis17.67094319
Mean0.9839486418
Median Absolute Deviation (MAD)0
Skewness1.634757873
Sum256418
Variance0.1750493368
MonotonicityNot monotonic
2022-04-28T15:36:35.153999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1226115
86.8%
020862
 
8.0%
211294
 
4.3%
31802
 
0.7%
4389
 
0.1%
5104
 
< 0.1%
622
 
< 0.1%
77
 
< 0.1%
94
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
020862
 
8.0%
1226115
86.8%
211294
 
4.3%
31802
 
0.7%
4389
 
0.1%
5104
 
< 0.1%
622
 
< 0.1%
77
 
< 0.1%
82
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
94
 
< 0.1%
82
 
< 0.1%
77
 
< 0.1%
622
 
< 0.1%
5104
 
< 0.1%
4389
 
0.1%
31802
 
0.7%
211294
 
4.3%
1226115
86.8%
020862
 
8.0%

has_secondary_use
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
231445 
1
29156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0231445
88.8%
129156
 
11.2%

Length

2022-04-28T15:36:35.392544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:35.553111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0231445
88.8%
129156
 
11.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_secondary_use_agriculture
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
243824 
1
 
16777

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0243824
93.6%
116777
 
6.4%

Length

2022-04-28T15:36:35.706761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:35.865569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0243824
93.6%
116777
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_secondary_use_hotel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
251838 
1
 
8763

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0251838
96.6%
18763
 
3.4%

Length

2022-04-28T15:36:36.018759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:36.177755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0251838
96.6%
18763
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
258490 
1
 
2111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0258490
99.2%
12111
 
0.8%

Length

2022-04-28T15:36:36.329076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:36.488864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0258490
99.2%
12111
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260356 
1
 
245

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0260356
99.9%
1245
 
0.1%

Length

2022-04-28T15:36:36.651247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:36.915398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0260356
99.9%
1245
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260507 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0260507
> 99.9%
194
 
< 0.1%

Length

2022-04-28T15:36:37.064440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:37.224756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0260507
> 99.9%
194
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260322 
1
 
279

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0260322
99.9%
1279
 
0.1%

Length

2022-04-28T15:36:37.372681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:37.534167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0260322
99.9%
1279
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260552 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0260552
> 99.9%
149
 
< 0.1%

Length

2022-04-28T15:36:37.682891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:37.842637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0260552
> 99.9%
149
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260563 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0260563
> 99.9%
138
 
< 0.1%

Length

2022-04-28T15:36:37.995429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:38.158108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0260563
> 99.9%
138
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260578 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0260578
> 99.9%
123
 
< 0.1%

Length

2022-04-28T15:36:38.311001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:38.471475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0260578
> 99.9%
123
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
259267 
1
 
1334

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0259267
99.5%
11334
 
0.5%

Length

2022-04-28T15:36:38.623584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-28T15:36:38.784091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0259267
99.5%
11334
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-28T15:36:16.123136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:52.581020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:55.503742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:58.386171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:01.445218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:04.334170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:07.402224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:10.252235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:13.107989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:16.422239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:52.923914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:55.824034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:58.705459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:01.761074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:04.660335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:07.708114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:10.568663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:13.428004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:16.726638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:53.252962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:56.144908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:59.037877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:02.083150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:04.991886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:08.022225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:10.889135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:13.740717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:17.038614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:53.595857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:56.469974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:59.374442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:02.414717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:05.332913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:08.350920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:11.215105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:14.096344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:17.342767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:53.919462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:56.789019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:59.821934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:02.739003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:05.661094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:08.667183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:11.534263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:14.552277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:17.677069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:54.262551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:57.130418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:00.192717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:03.081638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:06.011148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:09.014636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:11.870531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:14.907185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:17.998570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:54.572989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:57.441514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:00.505327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:03.393587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:06.330622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:09.321466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:12.194540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:15.211885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:18.306197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:54.885263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:57.756927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:00.821523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:03.708599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:06.650570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:09.630771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:12.510425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:15.515360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:18.610679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:55.200456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:35:58.075485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:01.138356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:04.026484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:07.084231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:09.941144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:12.812335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-28T15:36:15.817452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-28T15:36:39.061096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-28T15:36:39.831967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-28T15:36:40.600224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-28T15:36:41.358930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-28T15:36:42.245138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-28T15:36:19.331642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-28T15:36:21.847089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_other
080290664871219823065trnfqtd11000000000v100000000000
1288308900281221087ornxqsd01000000000v100000000000
29494721363897321055trnfxtd01000000000v100000000000
3590882224181069421065trnfxsd01000011000v100000000000
420194411131148833089trnfxsd10000000000v100000000000
53330208558608921095trnfqsd01000000000v111000000000
672845194751206622534nrnxqsd01000000000v100000000000
747551520323122362086twqvxsu00000110000v100000000000
84411260757721921586trqfqsd01000010000v100000000000
99895002688699410134tinvjsd00000100000v100000000000

Last rows

building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_other
26059156080520368598012553nrnfjsd01000000000v111000000000
260592207683101382190322555trnfqsd01000010000v100000000000
2605932264218767861325135trnfqsd01000000000v111000000000
260594159555271811537601312trnfxjd00001000000v100000000000
2605958270128268471822085trnfqsd01000000000v100000000000
260596688636251335162115563nrnfjsq01000000000v100000000000
2605976694851771520602065trnfqsd01000000000v100000000000
2605986025121751816335567trqfqsd01000000000v100000000000
26059915140926391851210146trxvsjd00000100000v100000000000
260600747594219910131076nrnfqjd01000000000v300000000000